We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features.

we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets.


Recently, contextual information has been exploited for sarcasm detection (Wallace et al., 2015;Karoui et al., 2015).

contextual features extracted from history tweets by the same author has shown great effectiveness for tweet sarcasm detection (Rajadesingan et al., 2015;Bamman and Smith, 2015).


The structure of the model is shown in Figure 1(a), which consists of two main components, modeling the target tweet and its contextual tweets, respectively.

the local component (the left of Figure 1(a)) is used to extract features f from the target tweet content, and the contextual component (the right of Figure 1(a)) is used to extract contextual features f from the history tweets of the author.


Given an input tweet w 1 , w 2 • • • w n , we extract a set of sparse discrete feature vectors instantiating a set of feature templates over each word, respectively.


The final local feature vector f is the sum of f i from all words: We follow Bamman and Smith (2015) for defining the features of the contextual tweets.

a set of salient words are extracted from history tweets of the target tweet author, which can reflect the tendency of the author in using irony or sarcasm towards certain subjects.


Note that our baseline discrete model and neural model have highly similar structures, differing mainly in the use of manual discrete features and automatic neural features.

the discrete feature vector f = n i=1 f i can be regarded as being obtained by using a sum pooling function Here f i is a discrete feature vector with manual feature engineering.


This second scenario is the focus of the present work.

given two document collections containing the public declarations and speech transcriptions of two politicians, our goal is to assess whether the two agree or not on a topic.


We choose Debatepedia, an online encyclopedia of debates, because it provides statements from two opposing sides debating on well-defined, controversial topics.

for each topic, Debatepedia gathers a set of relevant evidences and statements, mainly from news, that are framed as being in favour or against a specific debate question, e.g.


Thus we can get sub-word level (morpheme-like) feature map.

the i th element of f is given by: where Q[:, i : i + m − 1] is the matrix of (i) th to (i + m − 1) th character embedding and g corresponds to ReLU non-linearity.


Inspired by word embedding training methods (Collobert et al., 2011;Mikolov et al., 2013), we take a negative sampling approach.

we take pairs of sentences that contain the same aspect phrase as positive training examples, and pairs of sentences that contain incompatible aspect phrase as negative training examples, maximizing a score margin between positive and negative examples.


contextual words based on their weight scores with respect to each aspect phrase.

given each word vector e i , which is projected into a word embedding matrix L w ∈ R d×|V | , where d is the dimension of word vector and |V | is the size of word vocabulary.


To achieve this, we use a large-margin framework to restrict the distance, as proposed by Mignon and Jurie (2012).

sample pairs containing the same aspect phrase are used as positive instances and sample pairs with incompatible aspect phrase are used as negative instances.


We exploit lexical similarity to obtain incompatible aspect phrases, which have low similarity in semantic lexicon.

we choose WordNet as the semantic lexicon.


The generated sample aspect phrase pairs are fed into left and right sub neural network of Figure 1, respectively.

each training sentence is utilized with its labelled aspect phrase as a gold sample.


3 Given a MulCG of a question, we can execute it based on the KB by instantiating all variable vertices according to the constraints in order.

we start from the constant vertex in the basic query graph and instantiate all variable vertices according to the constraints in order.


This continuous space representation approach has shown better results compared to lexical matching approaches (e.g., word-alignment models).

for two sequences S l = (w 1 , w 2 , ..., w n ) and S r = (w 1 , w 2 , ..., w m ), we add " S " and " E " to the head and tail of them respectively to form an input S l and S r of the network.


The results show that our method outperforms the baseline on the test set of ComplexQuestions and have comparable result on the test set of WebQuestions.

the Constraint column in Table 4 indicates using constraints or not.


Here we focus on the first of them: question re-ranking in cQA.

given a query question q and a pool of previously-posted questions D, rank the questions in D according to their relevance against q.


Note that t(x, y) is not symmetric: t(y, x) would return the tree related to y enriched with REL tags.

we apply a partial tree kernel (PTK) as the base kernel K T , which counts the number of shared subtrees between x 1 and x 2 (Moschitti, 2006a).


Word embeddings are represented in blue color, while the avg, std, min and max descriptors are indicated in red, yellow, green and purple colors, respectively.

we use gray color to denote the hidden layer.


3 Additionally, this autoencoder can naturally handle variable-length sentences (Notice that g ∈ R d ).

we concatenate the input descriptors into one vector, and encode it into the hidden layer shown as gray nodes in Figure 3: where W enc ∈ R m×2d and b enc ∈ R m are the encoding parameters.


In this section, we carried out a series of experiments to validate the effectiveness of our proposed bilingual autoencoders on NIST Chinese-English translation tasks using large-scale bilingual training data.

we investigated 1) whether our model is able to distinguish parallel sentence pairs from nonparallel sentences, and 2) whether our model can improve machine translation quality.


Table 3 summarizes the results, from which we observe that: 1) Our model can significantly improve translation quality on all testsets for any dimensions.

aS2MM model obtains the best result 28.08 when d = 100, which outperforms Baseline and BBoWaE model by up to 1.46 and 1.1 BLEU points respectively.


The main objective of this paper is to explore how to mitigate the underfitting problem for infrequent relations -without manually creating labeled data for those relations.

we aim to exploit the availability of a much larger amount of unlabeled data.


Furthermore, we can also control the filtering threshold score at a finer granularity.

if the parser allows a user to feed a partial discourse tree or even just some nodes for training, we can set the filtering threshold on each node instead of each document.


For the unlabeled documents, we used 2000 Wall Street Journal articles from the Penn Treebank dataset (Marcus et al., 1993).

the gold standard dataset and unlabeled dataset are from the same source; but there is no document belonging to both.


The results shown in the figure are based on all the instances in the entire dataset.

the performance of the frequent relations, due to their much higher frequencies, completely dominates the performance of the infrequent ones.


Compared with the filtering threshold of 0.5 shown previously, there is further improvement when the threshold is raised to 0.6 and 0.7.

from Figure 6(b), there is considerable improvement across the top-8 infrequent relations.


All the filtering experiments above are performed at the document level.

we calculate an agreement/confidence score for an entire discourse tree of a document, and if the score passes the threshold, this entire discourse tree along with every node on it will be added to the new training instances, even though some nodes on this tree may have low scores.


In our experiments, we adopt one of two different sample configurations: the first focusing on a local context and the second extending the context to encompass one or more sentences.

for the former, we define a sample to be the set of tokens from the previous head noun of a noun phrase up to and including the head noun of the current noun phrase.


In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.

our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively.


Their basic idea mainly lies on unique characteristics in reader emotion analysis, different from the case in writer emotion analysis.

apart from the source text (e.g., news text), another type of text, the response text (e.g., comment text) written by the reader as a response to the source text, is available to help determine the reader emotion of the source text.


Finally, we assign a variable weight between each source text sample and its response text sample to address the information insufficiency in the response text.

we design a length-sensitive linear function to calculate the transition probability between the source and response text samples.


What we see in Figure 3 can be ascribed to the fragmentary view of the data created because of the hashtag-based process of bootstrapping a training set.

the small number of tweets, which we harvest, covers only part of the test-data distribution.


To this end, we leverage on VerbNet (Schuler, 2005), the largest English verb lexicon currently available, and Sentic LDA (Poria et al., 2016c), a classification framework that integrates commonsense in the calculation of word distributions in the linear discriminant analysis (LDA) algorithm.

we use a semi-supervised version of Sentic LDA in order to incorporate both supervised (VerbNet-labeled) and unsupervised information in such a way that a proper semantic space which reflects the desired information (verb concepts) is obtained.


In the future, we plan to discover new conceptual primitives in a more automatic and scalable way by means of dependency-based word embeddings.

we will exploit the internally-learned context embeddings of the skip-gram model in conjunction with the standard target word embeddings, to weigh context compatibility together with word similarity.


We use negative sampling to reformulate the objective function, which is then optimized through stochastic gradient descent (SGD).

the likelihood of each context entity of a target entity is defined with the softmax function in Eq.


Since we have representation of categories, we propose nearest neighbor (NN) classification to categorize concepts by directly comparing concept vectors with candidate category vectors.

given a set of concepts E and a set of candidate categories C, we convert all concepts to concept vectors and all candidate categories to category vectors.


Then we use the equation c = argmin c i ∈C ||c i − e|| to assign the concept vector e with category c. Since purity works as a standard evaluation metric for clustering (Rothenhäusler and Schütze, 2009), to compare our model with the concept clustering, we also use purity to measure our model's performance.

purity is defined as: where Ω denotes a clustering solution, G is a set of gold standard classes, n is the number of instances, k is the cluster index and j is the class index, ω k represents the set of labels in a cluster and g j is the set of labels in a class.


If i is 1, the word w is generated according to topic-word distribution P (w|z ds , C w , M ), which is defined as follows: In Eq.

(1), C w stands for the sliding window for w. C w contains several words that precedes w. where M = {v w , v z } stands for the word embedding and the topic embedding, x w is the result of element-wise addition of the word embeddings of C w and the topic embedding indexed by z ds (i.e.,  In Section 4.1, we describe how to sample the latent topics for sentences.


This evaluation shows that the filter f with a consistently fixed depth T can be trained to efficiently filter noise for word embeddings.

the number of stages T exceeds a consistent number T (with T > 3 in our case), leading to the loss of salient information in the vectors.


In distributional semantics, words are represented by context features, usually co-occurrence numbers between words or the pointwise mutual information between each word and each context word.

it turns out that words with a similar meaning have similar vectors of context features; semantically similar words occur in similar contexts (Rubenstein and Goodenough, 1965;Saif and Hirst, 2012;Bullinaria and Levy, 2007;Turney and Pantel, 2010;Bullinaria and Levy, 2012;Kiela and Clark, 2014).


Our work, which employs CNN-and LSTM-based deep neural networks, is inspired by advances in sentence classification (Kim, 2014) and sequence classification (Hochreiter and Schmidhuber, 1997) using distributional word representations and deep learning.

our approach leverages word2vec 1 distributional embeddings, dependency context-based embeddings (Levy and Goldberg, 2014), and factuality/certainty-indicating embeddings for improving claim classification.


Instead of generating a term by document matrix, where each term is tf • idf (product of term frequency and inverse document frequency), we compute a term by document matrix M D ∈ m×l , where each term in the matrix is the product of the projection of a word onto a target VNC and inverse document frequency.

the term frequency (tf) of a word is replaced by the projection (inner product) of the word onto σ vn (1).


Here we provide manually annotated training data.


One possible explanation is that the spectral norm evaluates the difference when two matrices act on the maximal variance direction, while the Frobenius norm evaluates on a standard basis.

frobenius measures the difference along all basis vectors.


(2009)'s, , and Li and Sporleder (2010b)'s models (see Table 4).

our method is especially effective when idioms are highly idiomatic.


In this section, we will briefly introduce RNN and CNN and then subsequently describe RNN and CNN based architectures for bi-sequence classification tasks.

we talk about five different ways of handling context along with the target sentence.


As a side-effect, if we choose m ′ < m, the dimensionality is reduced from m to m ′ .

note that this is said to be unbiased because given ⟨x, on average, prediction using a hashed map space is the same as the original version.


Thus, if we perform L1-regularized learning as defined in 1 of Definition 4.1, Ng's theorem implies that Equations (6) and (7) hold, satisfying f 1 (m) = 0 and f 2 (m) = 0.

the above theorem implies that if we add the conditions defined in Equation (4)) to Ng's theorem, it is possible to obtain a succinct representation (Equation (5)) whose predictive performance is nearly as good as that of Ng's theorem, albeit with a probabilistic penalty f 1 (m) and an error penalty f 2 (m).


First, we want to answer whether there are differences between textual and visual representations in the type of attributes that they encode; and second, where these differences are.

we present an inter-modality analysis while Rubinstein et al.


In both classification and regression we perform 2 runs of 5-fold stratified cross validation.

we create 5 (stratified) disjoint splits, repeating it with two different seeds.


In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations.

while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs.


The main goal of this work is to assess the contribution of dense vector representations of words and word pairs to temporal relation type classification, as detailed in Section 3.

we want to establish (i) which vector combination schemes are more suitable for classifying pairs of events, (ii) how well word embeddings can be used for this particular task compared to traditional features (Section 4.2), and finally, (iii) whether the combination of traditional features and word embeddings yields a better performance than using the two components in isolation.


Our goal is to compare classification performance on temporal relations using traditional features and word embeddings, which have recently shown to achieve good generalisation capabilities in several NLP tasks.

we want to evaluate whether, and in which configurations, they can contribute to advance state-of-the-art performance on temporal relation classification.


In this study, as a first step, we restrict the context information to PASs along the lines of the previous studies that utilize event-to-event relations in an anaphora resolution (Inoue et al., 2012;Peng et al., 2015).

we first assume a context of a query argument be a single PAS which takes the query argument as its argument (referred to as a context-PAS).


In the simplest case, this is done by element-wise operations on the vectors of the words (Mitchell and Lapata, 2010).

the vector representation − → w of a sequence of words w 1 , .


As an example, consider the simple case of a matrix multiplication between a m × n matrix M and a n × 1 vector − → v .

matrix m can be seen as a list of column vectors The result of the matrix multiplication is a combination of scalar multiplications of each element v i of the vector − → v with the corresponding column vector − → w i of the matrix m. we have: By denoting v i a feature whenever it is non-zero, we obtain an equivalent form as follows: The above notation says that we collect features of each − → w i vector but only up to "featureness" of v i , that is up to v i being non-zero.


We also present results for a least squares fitting model, which approximates the distributional behaviour of holistic phrase/sentence vectors along the lines of ( Baroni and Zamparelli, 2010).

for each verb, we compute an estimator that predicts the ith element of the resulting vector as follows: Here, the rows of matrix X are the vectors of the subjects (or objects) that occur with our verb, and − → y i is a vector containing the ith elements of the holistic phrase vectors across all training instances; the resulting − → w i 's form the rows of our verb matrix.


While sentence intersection was originally proposed for abstractive summarization, we argue that it is an interesting generic paradigm for modeling information overlap, which generalizes over several previous approaches.

it is expressive enough to capture partial semantic overlaps that are not modeled by textual entailment or even partial textual entailment (Nielsen et al., 2009;Levy et al., 2013).


Definition Given a sentence s, we can say that t p (p for premise), a subtree of s, entails some other subtree t h (h for hypothesis) in the context of s, if: s σ (s, t p , t h , α), where α assigns the slots of t h , and is textual entailment.

if s is true, then replacing t p with t h in s should create a new sentence that is also true.


To address the above two issues, this paper proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the interdependencies between predicates.

we propose a graph-based representation-Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates.


To address the above two problems, this paper proposes a graph-based method, which can effectively exploit both the context of a predicate and the inter-dependencies between predicates for accurate inference rule discovery.

we propose a graph-based representation, called Predicate Graph, which can capture the semantic relevance between predicates and features by encoding both the predicate-feature co-occurrence statistics and the rich inter-dependencies between predicates.


We use the same edge weight α for all semantic-dependent edges, and which will be empirically tuned.

given a predicate p, we find its semantically similar predicates as follows: 1) we identify its active/passive verb form as its semantically similar predicate, e.g., 'Y be acquired by X' will be identified as a semantically similar predicate of 'X acquire Y'; 2) we generate semantically similar predicate candidates by replacing each verb/noun in the predicate p with its synonyms/hypernyms in WordNet 3.0.


In this section, we describe how to accurately represent a predicate in a specific context.

given a predicate p, its context c and all features {f1, f2, …, fn}, we represent predicate p as a vector: where is the relevance score between predicate p and feature fi under context c. In following we first develop a context-insensitive random walk algorithm which can estimate context-insensitive relevance score between a predicate p and a feature f, then we extend the algorithm by taking context into consideration.


In this paper we estimate the context-insensitive relevance score between two nodes using one of the most widely used algorithm -Random Walk with Restart (RWR) (Tong et al., 2006), which can be fast computed and has been successfully used in many applications, like personalized PageRank (Haveliwala, 2003), image retrieval (He et al., 2004), etc.

rWr models the relevance score between node i and node j in a graph G as the steadystate probability ri,j, i.e., the probability of a random walk starts from node i will end at node j.


To learn context-specific predicate representations, we extend RWR algorithm by also taking context into consideration.

the start point of our algorithm is to distinguish context relevant information from context irrelevant information.


To discover inference rules, we first compute similarities between predicates, then two predicates p and q in context c will form an inference rule if their similarity is above a threshold.

because each representation ⃗ can be viewed as a distribution over nodes, we measure the similarity between two predicates using the Kullback-Leibler divergence between ⃗ and ⃗ (Kullback & Leibler, 1951): Notice that KL divergence is a distance measure: the smaller the KL divergence between ⃗ and ⃗ , the more similar the two predicates p and q.


In our implementation, the external vector s is a vector representation of sentence learned by a Seq-RNNs.

if the tree composer is Attentive Tree-LSTM, then the Seq-RNNs is Seq-LSTMs.


This is because for some of the hypotheses, the first word may not always have the highest score.

lSTM ( Figure 2) is a variant of RNN, which computes the hidden state h t using a different approach by adding an internal memory cell c t ∈ R n at every time step t. an lSTM unit considers the input state x t at time step t, the hidden state h t−1 , and the internal memory state c t−1 at time step Figure 1: Encoder-Decoder framework for sequence to sequence learning.


Given an English triple and a list of translated Chinese triple candidates, the method assigns a scalar to each translation pair to represent their semantic relatedness.

we represent each KB triple from the embeddings of words it contains, and introduce an adaptive composition model to effectively capture the semantic composition between arguments of a triple.


The gate takes v e and v r as inputs, and outputs as a weight α ∈ [0, 1], which linearly weights the two parts.

the gate is calculated as Equation 3, where σ is standard sigmoid function, W eg , W rg and b g are parameters.


To this end, we need to formalize a scoring function f (t e , t cj ), which is capable of measuring the semantic relatedness between an English triple t e and a Chinese candidate triple t cj .

we apply the continuous triple vector learned in Section 2.2.3 as English and Chinese triple representations without any feature engineering.


This empirical setup means that the importance of topics is much more influenced by controlled keyphrases than other topics, and that the importance of controlled keyphrases is equally influenced by controlled keyphrases and topics.

the domain has a positive influence on the joint task of keyphrase extraction and assignment.


In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges.

we model the requirements based on appropriate sources of evidence to be used for the QA task.


The PAD metric (Ben-David et al., 2007) is defined as 2(1 − 2 ), where is the generalization error and obtained by measuring how distinguishable are the two domains.

we use the learned representation to accomplish domain recognition task.


We use LDA to learn the topic structures of microblogs and the model is trained offline.

given a set of microblog posts S, where each post s ∈ S contains N s words {w s,1 , w s,2 , ..., w s,Ns }, LDA makes the following assumptions.


Next, we will introduce how we obtain [a 1 , a 2 , ..., a N ] in detail.

for each h j , we use the following equation to compute scores on how well the inputs around position j and the topic distribution θ s match: where K is the number of topics, W a ∈ R d×K , U a ∈ R d×d and v a ∈ R d×1 are the weight matrices.


In this paper we build on the abstractions made available in Saul programming language (Kordjamshidi et al., 2015), in order to create a unified and flexible machine learning programming framework using the generic Constrained-Conditional-Model (CCM) paradigm (Chang et al., 2012).

we build upon Saul's graph-based data representation and enrich it with primitive structures and sensors for the NLP domain that facilitate operating at arbitrary levels of 'globality' for learning and inference.


For example, a programmer can easily decide if he or she needs to operate at document, sentence, or phrase level.

without any programming effort the user can specify at which level of granularity the context should be considered, and how 'global' should learning and inference be.


Inputs x and outputs y are sub-graphs of the data-model and each learning model can pick specific correlations and substructures from it.


This representation corresponds to an integer linear program, and thus can be used to encode any MAP problem.

the g function is written as the sum of local joint feature functions which are the counterparts of the probabilistic factors: where C is a set of global constraints that can hold among various types of nodes.


This paper exhibits the capabilities and flexibility of Saul for solving problems in the NLP domain.

it shows how a unified predefined NLP data-model can help performing various tasks at various granularity levels.


Encoder The encoder in the proposed model is utilized in the character level.

the model is designed to compose the word-level information from the input sequence of characters.


To solve these problems, we propose a convolution enhanced bilingual recursive neural network (Con-vBRNN), which exploits word alignments to guide the generation of phrase structures and then integrates embeddings of different linguistic units on the phrase structures into bilingual semantic modeling.

we develop a new recursive neural network, in which the composition criterion for tree construction is the degree of consistency to word alignments rather than the reconstruction error.


To achieve this goal, we first use the structural alignment consistency (SAC) (Su et al., 2015) that is the basis of our model to classify resultant nodes of semantic compositions into two categories.

if the node n covers a sub-phrase, and there exists a sub-phrase in the other language such that these two sub-phrases are consistent with word alignments (Och and Ney, 2003), we say n satisfies the structural alignment consistency, and it is referred to as an SAC node, otherwise, it is a non-SAC node.


Next we will introduce a folding layer to exploit these dimensions simultaneously.

folding Layer This layer bridges the gap across different dimensions through averaging each nonoverlapping neighboring rows in the convoluted feature map C. for each row index r (1 ≤ r ≤ d 2 ), the output can be computed as follows (shown in the orange color in figure 3): where .


Overall, ConvBRNN obtains a gain of up to 2.1 BLEU points on average over the Baseline.

on the MT08 data set, the improvements over the Baseline can be up to 2.37 BLEU points.


Inspired by the supervised reordering in conventional SMT, in this paper, we propose a Supervised Attention based NMT (SA-NMT) model.

similar to conventional SMT, we first run offthe-shelf aligners (GIZA++ (Och and Ney, 2000) or fast align (Dyer et al., 2013) etc.)


To train NMT1, NMT2 and SA-NMT, we employed the same settings for fair comparison.

except the stopping iteration which was selected using development data, we used the default settings set out in (Bahdanau et al., 2015) for all NMT-based systems: the dimension of word embedding was 620, the dimension of hidden units was 1000, the batch size was 80, the source and target side vocabulary sizes were 30000, the maximum sequence length was 50, 6 the beam size for decoding was 12, and the optimization was done by Adadelta with all hyper-parameters suggested by (Zeiler, 2012).


Specifically, except the stopping iteration which was selected using development data, we used the default settings set out in (Bahdanau et al., 2015) for all NMT-based systems: the dimension of word embedding was 620, the dimension of hidden units was 1000, the batch size was 80, the source and target side vocabulary sizes were 30000, the maximum sequence length was 50, 6 the beam size for decoding was 12, and the optimization was done by Adadelta with all hyper-parameters suggested by (Zeiler, 2012).

for SA-NMT, we employed a conventional word aligner to obtain the word alignment on the training data before training SA-NMT.


The second aggregation approach computes quality by averaging over only the collected manual segment scores, using the automatic scores to choose what segments should be manually evaluated.

we expect segments that are predicted to be close to the average across segments to be most indicative of overall quality.


This choice is motivated by our definition of overall quality as the averaged segment-level quality.

we sort the segments by their automatic scores, and add the median 2 segment to SEL, the set of segments to annotate.


To the best of our knowledge, we are the first to be dedicated to systematically investigating whether supersenses can be used in SMT to alleviate source word sense translation errors.

we try to model supersenses for SMT in the following two ways: • A maximum entropy (MaxEnt) based model: Building multiple MaxEnt classifiers with one classifier per source word type, which incorporate supersenses as features.


The model takes a sequence labeling approach to learn a model for supersense tagging.

we employ a sequential labeller Supersense WN Senses Gloss social 1 give help or assistance; be of service social 6 contribute to the furtherance of body 2 improve the condition of stative 3 be of use stative 4 abstain from doing; always used with a negative change 8 improve; change for the better consumption 5 help to some food; help with food or drink consumption 7 take or use that is based on a Hidden Markov Model (HMM) trained in a discriminative way.


Specifically, we employ a sequential labeller Supersense WN Senses Gloss social 1 give help or assistance; be of service social 6 contribute to the furtherance of body 2 improve the condition of stative 3 be of use stative 4 abstain from doing; always used with a negative change 8 improve; change for the better consumption 5 help to some food; help with food or drink consumption 7 take or use that is based on a Hidden Markov Model (HMM) trained in a discriminative way.

the model can be seen as a perceptron-trained HMM (Collins, 2002) that jointly models observation/label sequences.


In this way, not only centered word c and its supersenses s c are included, but the preceding and succeeding k words with their corresponding supersenses are also involved.

c −k is the kth preceding word of c and s c −k is the supersense of word c −k .


We also divided our test set batch2 into two parts equally batch2a and batch2q respectively.

we used three test sets to verify the effectiveness of our proposed models.


There is a potential problem for sentence level adaptation: different parts of a sentence may belong to different domains.

it is possible that a sentence is overall out-of-domain, although part of it can be in-domain.


In this subsection, Q in of adapted and original phrases are also adopted in SMT decoding.

q in (E|F ) is added as a feature for adapted and original phrase pairs in PT and q in (E) of adapted and original n-grams are interpolated with n-gram LM probabilities.


Since our model is Bayesian, we put priors on all parameters.

the translation parameters (θ e ), language model parameters (θ f ) and jump parameters (θ a ) are drawn from Dirichlet distributions with parameter vectors α, β and γ.


Previously this means a simple postprocessing step during evaluation, but in our setting we can bring the trick to training.

using Euclidean distance as the ground distance, we would have parametrized C ts in the EMD term (4) as Considering the context vectors U S and U T , we now reformulate the ground distance as This modification affects both steps in the alternating optimization procedure.


We observe significant performance gains over both baselines, as measured by all evaluation metrics.

comparing our approach with the MONO+SEED baseline highlights the effectiveness of introducing the EMD program into the training phase.


The results are summarized in Table 1, where we merge the different tagsets for German and English into common tags to make them comparable.

all different types of adjectives fall into a single ADJ tag.


This section describes the foundations of our experiments.

we address preprocessing, which is an important part of the machine learning pipeline, and the setup of our NMT training.


Our models were trained on the data provided by the 2014 Workshop on Machine Translation (WMT).

we used the Europarl v7, Common Crawl, and News Commentary corpora.


We address this issue by leveraging deep neural models to extract rich semantic features from news text.

a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures.


We identify a subset of the key concepts in the question for use in feature extraction.

we focus on concepts identifiable using part-of-speech tags and grammatical dependency relations, which covers the vast majority of key concepts.


The challenge stems in part from the fact that an event coreference resolver typically lies towards the end of the standard information extraction (IE) pipeline, assuming as input the noisy outputs of its upstream components.

a standard event coreference resolver takes as input the extracted event triggers, their arguments, and the entity coreference information, and aggregates this information through rules to resolve coreferent event mentions.


On both corpora, our approach performs significantly better than the baseline pipeline-based resolver.

on the KBP corpus, we achieve the best result reported to date surpassing the previous best result by around 0.43 percentage points in average F1-score.


creativecommons.org/licenses/by/4.0/ Second, KBP adopts a more relaxed definition of event coreference than ACE.

kBP requires that two event mentions be coreferent as long as they intuitively refer to the same real-world event.


We train this model using CRF++ 2 , treating each sentence as a word sequence.

we create one instance for each word w and assign it a class label that indicates whether it begins an entity mention with type t j (B-t j ), is inside an entity mention with type t j (I-t j ), or is outside an entity mention (O).


Recall that since each (multi-)word may trigger multiple event mentions having different (sub)types, we train one CRF for each type.

to train the CRF for type t j , we create one instance for each word w i , assigning it a class label that indicates whether it begins a trigger with subtype s jk (B-s jk ), is inside a trigger with subtype s jk (I-s jk ), begins a trigger with other types (B-t m =j ), is inside a trigger with other types (I-t m =j ) or is outside a trigger (O).


To improve the recall of event trigger detection, we augment the CRF output with heuristically extracted triggers.

we first construct a wordlist containing triggers that appear infrequently (less than 10 times) in the training data and do not belong more than one subtype according to the training data.


To address this issue, we implicitly encode the high-dimensional features by embedding them as weighted unit clauses, one for each grounding of the hidden and query predicates.

for each hidden/query ground atom X i , we derive a weight φ(X i ) using the baseline system.


Fortunately, we can exploit the structure of the MLN given in Figure 1 in order to scale up MAP inference.

the subset of ground atoms corresponding to two distinct documents are independent of each other.


Similar to what we observed on the KBP corpus, the consistently superior performance achieved by the MLN-based resolver can be attributed to its substantially higher recall accompanied by a slightly lower precision.

since MUC is a link-based metric, the fact that the MLNs achieve a higher MUC recall on both datasets suggest that the MLNs are better at discovering event coreference links than the baseline.


For both languages, our trigger classifier had difficulties with correctly classifying certain frequently-occurring words that are sometimes used as triggers and sometimes not.

the classifier misclassified many non-trigger instances of these words as triggers, which were subsequently used to establish coreference links by our resolver.


In this paper, we propose a corpus fusion approach to corpus fusion for emotion classification with ILP optimization.

we employ intra-task and extra-task constraints to better capture the similarity between two different emotion taxonomies and address the different annotation guidelines.


The sentence representation can be naturally considered as the feature to predict the sentiment polarity of sentence.

each word is represented as a low dimensional, continuous and real-valued vector, also known as word embedding (Bengio et al., 2003;Mikolov et al., 2013;Pennington et al., 2014;.


We believe that capturing such target-dependent context information could improve the accuracy of target-dependent sentiment classification.

we use two LSTM neural networks, a left one LSTM L and a right one LSTM R , to model the preceding and following contexts respectively.


In this paper, we take advantage of the "socialization" of third party websites to build the ground truth collection.

we map one's Weibo (the largest microblogging service in China) account to his or her corresponding Dianping (the largest consumer review site in China) account.


The aim of the learning task is to derive a prediction function f such that, for each feature vector In this section, we discuss features extracted from Weibo service.

we study how to derive effective latent features for the task.


Since traditional survey-based consumption behavior analysis task mainly focus on demographic attributes (Jang et al., 2004;Fodness, 1994), in this paper, we refer to the results obtained with feature METADATA as baseline.

for baseline features, we use logistic regression to combine all features together, which is a one layer classifier; for the RAW features and LATENT features, we construct a two layer classifier to combine heterogeneous features, i.e., in the first layer, we construct base classifiers using single feature sources, then we build a second layer classifier on top of the first layer classifier which takes the output of base classifiers as input.


We built "topic-label" database over the last two data sets by our OLLDA algorithm.

for CONF, we trained OLLDA over the data from CIKM2013 to obtain probability distributions with labels as test queries, and trained OLLDA over the remaining data as "topic-label" database; for TW, to increase the number of points with same labels in a probability vector set, we split the dataset into 12 pieces according to the time order, and trained separately OLLDA over the 1 th ∼ 11 th pieces as "topic-label" database, and over the 12 th piece as test queries.


It is then natural to decompose the probability starting from the given word.

the predicted keyword k splits a reply into two (sub-)sequences: Backward sequence: Forward sequence: and the joint probability of remaining words can be written as where p(---|r k , q) refers to the probability of the backward and forward subsequences given the split word r k and an encoded query q.


Our previous experiments show the asyn-B/F is the most natural way of modeling backward and forward sequences, and thus we adopt this variant in seq2BF.


It is important that such systems are able to utilize the information contained in team discourse to gain insight into speakers' cognitive and performance states, and to make better predictions about the course of the interaction.

the finding that self-repairs are strong indicators of collaborative processes and are increasingly utilized in effective teams, suggests that the detection and interpretation of speech disfluency can be of great benefit to dialogue systems.


Notable discriminative approaches are conditional random fields (CRFs) (Lafferty et al., 2001) and structural SVM (Tsochantaridis et al., 2005).

structural SVM has built a very strong reputation for experimental accuracy in NLP tasks (Joachims et al., 2009;Tang et al., 2013;Qu et al., 2014) and for this reason we exploit it in our NER pipeline.


The goal of this work was to develop an open-source information extraction framework for scientific articles using generic patterns present in various publication formats.

we extract metadata information and section related information.


While our model is in line with related work on evidence types (Rinott et al., 2015), we assign a type to each unit in order to capture an editorial's overall argumentation strategy.

we see argumentative discourse units as the smallest elements of the argumentative discourse of an editorial.


While overruling some decisions of the annotators, we thereby achieve a more consistent annotation, which is crucial for learning based on the corpus.

we conducted the following post-processing steps: • A considerable number of segments was annotated as no unit, although it should have been merged with the next segment.


Rather, we present some basic findings that indicate the potential of our corpus for analysis in this regard.

table 5 shows detailed statistics about the types of argumentative discourse units in the corpus.


This paper contributes to a growing body of evidence that-when coupled with appropriate machine-learning techniques-linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data.

we show that phonological features outperform character-based models using the PanPhon resource.


Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks.

one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.


Compared with the state-of-the-art models, BLSTM-2DCNN achieves excellent performance on 4 out of 6 tasks.

it achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.


The word embeddings are pre-trained on much larger unannotated corpora to achieve better generalization given limited amount of training data (Turian et al., 2010).

our experiments utilize the GloVe embeddings 7 trained by Pennington et al.


We choose the following: we first represent each node (pivot language word) in a graph as the vector of distances to all other nodes.

to u ∈ is then defined as the average distance (Euclidean norm) of the so represented nodes in the graphs: Finally, we define the (syntacto-)semantic distance D( , ) of two languages and as the average graph distance over all N − 1 pivots (l and l excluded): By summing over pivots, we effectively 'integrate out' the influence of the pivot language, leading to a 'pivot independent' language distance calculation.


We note a grouping together of es, pt, fr, en, it; nl, da, de, sv; fi, et; ro, bg, el; hu, pl, cs, sk, sl; and lt, lv.

{es, pt, fr, it, en} appear to form a homogeneous group with, consequently, similar semantic associations, as captured by word embeddings.


In our case, this may relate to semantic loans (adopting the semantic neighborhoods of loaned words within the target language) rather than to structural or grammatical borrowings.

geographically related languages exhibit a higher probability to borrow words from each other together with the same range of semantic associations.


We achieve this by applying our novel residual bypass function to our character representations, inspired by the success of ResNets.

we first apply the bypass to a CNN-based model achieving large gains over a plain CNN, and later evaluate its effectiveness in a ResNet.


Pre-trained word embeddings are unlikely to include entries such as 'International Organization for Migration', so we apply a simple heuristic in order to avoid treating most MWEs as unknown words.

the representation of a MWE is set to the sum of the individual embeddings of each constituent word.


We address the issue of high dimensional feature spaces when dealing with syntactic features, by using word and dependency feature embeddings.

we use the same skip-gram based model to create representations of all three context types.


During the training process, we use only the mentions within the distance of 15 or less.

we consider the pairs with 14 or fewer mentions between them.


Indigenous languages could be greatly benefit from this kind of research in the future.

american languages with rich morphology, e.g., agglutinative and polysyntehthic, seems to benefit from approaches that take into account the morphology and sub-word modeling.


In order to alleviate the domain mismatch, we are essentially looking for a feature extractor that only captures the semantics of the event types but not the difference in language usage between English and the target language.

we want the features captured by CNN to be informative for the event type classification and to be language-invariant at the same time.


Our proposal is to capture both types of clues by incorporating a max pooling layer on top of the base model.

as illustrated in Figure 3a, for each word w t , we integrate the intermediate representation of label prediction for each predicate h K i,t by applying max pooling to the vectors that represent pairs of prediction information for two predicates h K i,t and h K j,t (including the case i = j): In this equation, maxpool is an operation to extract the maximum value of each dimension in The newly obtained vector h i,t for w p i and w t is input into the softmax layer in the same manner as in the base model.


(2017), whereas d r = 256 in our experiments.

we speculated that the base model with a low dimensionality left a larger margin for improvement and incorporating the Grid architecture derived positive effects.


Highly local features have been used in recent state-of-the-art models Dozat and Manning, 2016;Shi et al., 2017).

dozat and Manning (2016) show that a locally trained arc-factored model can give the best reported accuracies on dependency parsing.


We investigate the problem by building a chart-based model that is local to unlabeled constituent spans (Abney, 1991) and CFG-rules, which have been explored by early PCFG models (Collins, 2003;Klein and Manning, 2003).

our models first predict unlabeled CFG trees leveraging biaffine modelling (Dozat and Manning, 2016).


The label generator (Figure 1c, 1d) predicts constituent labels for each span in the unlabeled tree using tree-LSTM models.

we design two different classification models for unlabeled parsing: the span model (Section 2.1) and the rule model (Section 2.2).


In the bottom, a bidirectional LSTM layer encodes the input sentence to extract non-local features.

we append a starting symbol <s> and an ending symbol </s> to the left-to-right LSTM and the right-to-left LSTM, respectively.


After obtaining the span representation vectors, we use these vectors to calculate the partition score ps k .


(2016) and Cross and Huang (2016b) to handle unknown words.

we first map all words (not just singleton words) in the training corpus into unknown word classes using the same rule as Dyer et al.


The number of training epochs is decided by the evaluation performances on development set.

we perform evaluations on development set for every 10,000 examples.


Due to its scarce practical applications, modeling grammaticality judgments with computers has never been a typical NLP task, but it can be an important testbed for theories of language processing and grammatical competence.

judgments on syntactic well-formedness require a sensitivity to long-distance structural cues which is a crucial aspect of language competence (Everaert et al., 2015).


As the lawmaking process in each state, and even within each chamber, is different, we divide the problem space by state and chamber, building separate models for each subset.

we consider each of these as separate problems: upper chamber bills and lower chamber bills.


LL equals zero for a perfect classifier, and increases with worse probability estimates.

ll penalizes models more the more confident they are in an incorrect classification.


Unlike prior work, we assume documents are trees, where every tree node u corresponds to a structural element and is labeled with text l(u).

the root is labeled with the document title, sections and subsections are labeled by their title, and paragraphs and sentences are labeled by the text they contain.


In this section, we describe a model for the agent and a training algorithm based on DQN .

we introduce a tree-sampling strategy, which addresses the exploration challenge stemming from the bias towards answers early in the document.


We also add some of the recent enhancements to DQN (Hessel et al., 2017), which have proved to be useful in our setup.

we implement Double Q-Learning (van Hasselt et al., 2016), Prioritized Experienced Replay (Schaul et al., 2015), and Dueling Networks .


Because our focus is on the navigation ability of the agent, we train a single RC model and fix it in all experiments.

we download RASOR Salant and Berant, 2018), 3 and exactly follow the procedure described by the authors of TRIVIAQA for training a RC model, i.e., we train RASOR on the first 400 tokens of each document in TRIVIAQA-NOP.


There is minimal supervision used during the speculation process.

we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.


In this paper, we describe TextEnt, a simple neural network model that learns distributed representations of entities and documents from a KB.

given a document in a KB consisting of words and contextual entities (i.e., entities referred from entity annotations in the document), our model predicts the target entity explained by the document (see Figure 1), and maps the document and its target entity close to each other in a continuous vector space.


For computational efficiency, we limited the size of our dataset.

we excluded documents with fewer than five incoming links from other documents if the corresponding entity of the document is not contained in the dataset used in our fine-grained entity typing experiments, presented in Section 3.1.


Our experimental setup follows that of Yaghoobzadeh and Schutze (2015).

we use their entity dataset of 201,933 Freebase 6 entities mapped to 102 entity types based on the FIGER type set (Ling and Weld, 2012).


The ranking measures test how well a model ranks entity types.

we ranked the entity types based on the probabilities assigned by the model and evaluated the ranked list using the precision at 1 (P@1) and breakeven point (BEP) 8 .


We used an MLP classifier with the entity representations as inputs to predict the probability of entity e being a member of type t in the set of possible types T .

we used an MLP with a single hidden layer and the tanh activation function, and an output layer that contains, for each possible type t ∈ T , a logistic regression classifier that predicts the probability of t: where c e ∈ R d is the vector representation of entity e, σ is the sigmoid function, and W h ∈ R h×d and W o ∈ R |T |×h are the weight matrices corresponding to the hidden layer and the output layer, respectively.


Our TextEnt-full model outperforms the baseline models in all measures.

the TextEnt-full model achieves a strong P@1 score of 93.2%, which clearly shows the effectiveness of our entity typing model for many downstream NLP tasks.


Furthermore, we automatically annotated entity mentions in the documents using an entity linking system.

we used TAGME 13 (Ferragina and Scaiella, 2010), a state-of-the-art entity linking system that is freely available and has been frequently used in recent studies (Xiong et al., 2016;Hasibi et al., 2016).


We now describe a probabilistic model that effectively uses new words incorporated into popular usage to estimate when the document was written.

our model computes the likelihood of observing a set of words that came into popular usage after year x given the document was written in year y to estimate when the document was written.


NEOProbBayes: We use a Bayesian scheme to incorporate all the observed evidence as follows: Let X = {X i for each year i}.

we compute the following: where we make the Naïve Bayes assumption that each X i is independent of any other X j , when conditioned on the year y.


To place our models in the context of prior work which use a large set of linguistic features, we consider two linguistically feature rich models.

we consider a bag-of-words based Naïve Bayes' model as well as a neural network based model NEURALDATE for this task.


The number of templates in a dataset reflects the diversity of problem types.

we have a subset setting T 6, which represents problems for which the associated template appeared equal to or more than six times in the subset.


c j is the weighted sum of the encoder hidden states.

w h , w s , U , b attn , and b are parameters of the model.


• We neuralize the traditional trend detection pipeline.

we represent obtained keyphrases as averaged tf-idf word embeddings and use a recurrent neural network (RNN) model to perform supervised learning for trend status.


As illustrated previously, it is expected that our model could make predictions of trending research fronts.

trends refer to research areas which are gradually being studied, hence, there should be a strong foundation for them, meaning that there are certain level of existing work associated with them; yet they should not be the mostly-discussed issue at the same time.


We assume that our prior knowledge of lexical entailment relations is contained in word pairs and their entailment relations.

let R denote a set of lexical entailment relations.


In this work, we only use a subset of these relations, namely, entailment ( ), reverse-entailment ( ), alternation ( ) and equivalence (≡).


To illustrate our idea, we first show that in the extreme case when word vectors we try to learn are binary vectors precisely representing set memberships, the semantic relation between two words can be determined by comparing the two vectors element-wise.

let D denote the size of the universe D. Since a word x can be regarded as a subset of D, we assume that the entailment vector of x is a D-dimensional binary vector x, where x i is 1 if the i th element in D is inside the set x and 0 otherwise.


This hidden layer essentially represents c 0,0 , c 0,1 , c 1,0 and c 1,1 , and it is used for the prediction of the relation between two words.

let w 1 , w 2 ∈ R d represent the entailment vectors corresponding to words w 1 and w 2 .


In this section, we describe how we derive labeled word pairs from WordNet to construct L. Because eventually we will test the learned entailment vectors on the SICK and SNLI textual entailment datasets, we focus on extracting word pairs with at least one word appearing in SICK or SNLI.

we follow the following steps to construct L. In all the steps, w 1 and w 2 refer to nouns found in WordNet.


(2) For the standard split of the datsets, both w2v+NN and w2v+ST can outperform w2v, and w2v+ST's accuracy is significantly better than w2v.

our model outperforms the state-of-the-art performance on the SICK dataset.


We call it neighbor prediction, which is widely used to model network structure (Tang et al., 2015a;Liao et al., 2017).

we utilize the self representation e i to predict the link probability between entity e i and other entities.


Therefore, we use type order to model different relatedness between an entity and its related types, which reflects the structural information of type hierarchy to a certain degree.

we define two kinds of type order as follow: Definition 5 Ancestor order: In the type-path of an entity, specific types should get higher score than its ancestor.


We model the structure of type hierarchy in a margin-based loss function.

ancestor order makes entities closer to specific types than general types, and sibling order makes the learned entity embeddings have strong discriminative power between sibling types.


It combines the advantages of attributed network embedding method and semi-supervised learning.

it employs a deep neural network architecture to integrate structure and attribute information of an entity.


For German readability assessment, however, little progress has been made in recent years, despite a series of promising results published around the turn of the decade (Vor der Brück et al., 2008;Hancke et al., 2012).

german readability research has suffered from the lack of a shared reference corpus and sufficiently comparable corpora for cross-corpus testing of readability models: While for English research, the Common Core corpus consisting of examples from the English Language Arts Standards of the Common Core State Standards, and the WeeklyReader corpus of online news articles have been widely used in studies on English readability and text simplification (Vajjala and Meurers, 2014;Petersen and Ostendorf, 2009;Feng et al., 2010), there are no comparable resources for german.


Cohesion measures are highly informative, too, although more so for Tagesschau/Logo.

the use of certain personal or possessive pronouns is highly informative for GEO/GEOlino.


We also plan to further investigate the linguistic properties of our two data sets.

the Tagesschau/Logo data set requires further statistical and qualitative analyses to investigate why its linguistic characteristics generalize well across all folds of the data set itself but not across GEO/GEOlino.


Ranklib 2 , a well-known learning-to-rank library in Java from the Lemur Project is used to build the ranking models.

we have used the LambdaMART algorithm to train our learning and ranking models.


In the future, we would like to run a long-turn study with arbitrary users and arbitrary texts using a freely available online tool.

for text simplification, the approach can be employed to provided graded complexity level of texts, as it is done for instance in the Newsela instructional content platform 5 .


LSTM-based language models have been shown effective in Word Sense Disambiguation (WSD).


Hence, we provide the knowledge-enriched word embedding (KEWE), which encodes the knowledge on reading difficulty into the representation of words.

we extract the knowledge on word-level difficulty from three perspectives to construct a knowledge graph, and develop two word embedding models to incorporate the difficulty context derived from the knowledge graph to define the loss functions.


In this paper, we provide the knowledge-enriched word embedding (KEWE) for readability assessment, which encodes the knowledge on reading difficulty into the representation of words.

we define the word-level difficulty from three perspectives, and use the extracted knowledge to construct a knowledge graph.


To model the relationship among words on reading difficulty, we first introduce how to extract the knowledge on word-level difficulty from different perspectives.

we consider three types of wordlevel difficulty: acquisition difficulty, usage difficulty, and structure difficulty.


Since the rating on AoA is an unsolved problem in cognitive science (Brysbaert and Biemiller, 2016) and not available for many languages, we explore extra materials to describe the acquisition difficulty.

we collect three kinds of knowledge teaching materials, i.e., in-class teaching material, extracurricular teaching material, and proficiency test material.


For the graph-based difficulty context, given a word w, we define its difficulty context as the set of other words that have relevance to w on difficulty.

we define two types of difficulty context, positive context and negative context, corresponding to the two types of edges in the knowledge graph (i.e., Sim edge and Dissim edge).


Since dissimilarity is generally not transitive, we adopt the immediate neighbor strategy to sample the negative context.

on the knowledge graph with only Dissim edge, we collect the negative context from the immediate neighbors of the target node w t and get the negative context list C n (w t ).


Given a text corpus, the Skip-Gram model aims to find word representations that are good at predicting the context words.

given a sequence of training words, denoted as w 1 , w 2 , • • • , w T , the objective of Skip-Gram model is to minimize the log loss of predicting the context using target word embedding, which can be expressed as follows: where s is the window size of the context sampling.


We adopt the stochastic gradient descent to train our models.

for the hybrid model (KEWE h ), we adopt the mini-batch mode used in (Yang et al., 2016).


The max layer is used to generate a fix-size feature vector from variant length sequences.

given a document represented by a matrix M ∈ R m×n , where the kth column is the word embedding of the kth word in the document, the max layer output a vector f max (M ): where t = {1, 2, .


Firstly, we study the impacts of the knowledge graph on the performance of KEWE k .

we study the following three parameters of the knowledge graph: the knowledge vectors generated from the three word-level difficulties (i.e., acquisition difficulty, usage difficulty, and structure difficulty), the similarity function (i.e., sim()), and the number of neighbors of each node in the knowledge graph (i.e., k).


In this paper, we investigate the possibility of enriching the reference section of a scientific Wikipedia article using the wikilinks present in the article as a surrogate.

we introduce the novel idea of reference inheritance from the source pages pointed to by the wikilinks present on the target page to populate the reference section of the target page with relevant references.


Therefore, we first create two sets of RHD from P and H to denote their corresponding structures, and then perform comparison between triplets in P and those in H. Accordingly, two RHD triplets should be aligned if their relations are related and their head-words are aligned (e.g., the triplets with the same indexes are aligned in Figure 1).

when two RHD triplets are compared, each part of RHD triplet (i.e., Relation, Head, and Dependent) should be compared separately.


Our model is inspired by their approach.


Table 1 shows that our model achieves an accuracy of 87.4%, which outperforms most of those models with tree structure (even those with complicated network architectures (Munkhdalai and Yu, 2016;Zhao et al., 2016)), and our model is more interpretable than the other models which are based tree structure.

our performance is better than Parikh et al.


Different from them, we transform P and H into two sets of RHD triplets, instead of comparing one RHD triplet of P with the merged information of the whole H (and vice versa), we directly compare a RHD triplet of P with another RHD triplet of H to obtain the individual comparison vector (without merging).

when comparing two RHD triplets, each part in RHD triplet (i.e., rel, head, and dep) is compared separately and cross-comparison is adopted between nodes.


Motivated by the above observations, we propose a novel cooperative denoising framework (see Figure 1) to leverage the corpus-based and KG-based information.

we design two base networks, Corpus-Net and KG-Net, which are modeled with two separate Gated Recurrent Unit (GRU) networks, to predict relations using word-sequence and entity-sequence respectively.


For instance, network embedding methods such as DeepWalk (Perozzi et al., 2014) primarily encode graph structure information, while knowledge embedding methods such as TransE (Bordes et al., 2013) usually focus on triples information, we can flexibly use one of them or merge them together.

we link the detected entity names in sentences to the Freebase5M (Bordes et al., 2015) by n-gram text matching, and use the DeepWalk and TransE embeddings of linked entity candidates denoted as {e 1,1 , e 1,2 , .


We devise a bi-directional knowledge distillation method to enhance their supervision information in label space.

the two base networks learn with the hard label y from distant supervision.


This is largely motivated by the adversarial mechanism which has been effectively applied to measure the similarity between distributions in a variety of scenarios, such as domain adaptation (Bousmalis et al., 2016) and multi-modal representation learning (Park and Im, 2016).

we build two counterpart networks, using convolutional neural networks (CNNs), for source language and target language, to generate the latent feature representations respectively.


To validate the language independence of our feature adaptation framework, we also implement our adversarial feature adaptation systems and the MT-based systems in Table 1 on the same corpus from the opposite direction.

we regard Chinese as the source language and English as the target, then learn the feature representations from the Chinese dataset to predict the relation labels of the English dataset.


For discriminative training, we use a max-margin framework for learning (or fine-tuning) parameters θ.

a scoring function ϕ(•, •; θ) is defined to measure the similarity between the corresponding representations of questions and answers.


Hence, we importantly replace this manually-tuned and static mixing ratio with a 'dynamic' mixing ratio learning approach, where a controller automatically switches between the tasks during training, based on the current state of the multi-task model.

we use a multi-armed bandits based controller with Boltzmann exploration (Kaelbling et al., 1996) with an exponential moving average update rule.


both models tend to skip these particles and to select the content words such as nouns and verbs.


Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines.

our model outperforms other baselines by more than 50% in the few-shot scenario.


Thus for a certain attribute, the label of a specific case and the label of the corresponding charge should be the same or not in conflict.

for a certain attribute, the label of a case and the charge can only be (Yes, Yes), (No, No), (Yes, NA), or (No, NA).


To address the problem of prediction few-shot and confusing charges, we introduce discriminative legal attributes into consideration and propose a novel attribute-based multi-task learning model for charge prediction.

our model learns attribute-free and attribute-aware fact representation jointly by utilizing attribute-based attention mechanism.


It adopts a pipeline framework, and greedily builds the discourse tree from the bottom-up.

at each step of the tree-building, a binary Support Vector Machine (SVM) classifier is applied to determine which pair of adjacent discourse constituents should be merged to form a larger span, and then another multi-class SVM classifier is applied to assign the type of discourse relation between the chosen pair of constituents.


They decomposed the problem of document-level discourse parsing into two stage: intra-sentential and multi-sentential parsing.

they employed two separate models for intra-and multi-sentential parsing.


In the macro discourse structure, we use the arrows pointing to represent the "Nucleus-Satellite" relations.

the edges with arrows point to the "Nucleus" units, and the edges without arrows point to the "Satellite" units.


To improve the accuracy of the structure identification, we enforce additional commonsense constraints in its Viterbi decoding.

we disallow the existence of all-zero sequences (at least one pair must be merged).


(2018) to calculate the semantic similarity (including the semantic similarity between adjacent discourse units and the similarity between the discourse unit and the topic).

in order to prevent the sparsity of the features, we discretize the semantic similarity into 10 levels.


(C1) Nuclearity type constraints: the task of nuclearity recognition is a single-label classification problem.

the label of an instance could be only one option.


Data: We use the the corpus annotated by ourselves for experiment, and the detailed corpus data is described in the  There are 8,863 instances in MCDTB, and we use fivefold cross-validation to ensure the objectivity of the experiment.

we divide the articles into 5 datasets and assign articles of the different lengths (length means the number of paragraphs of a discourse) into the 5 datasets relatively equally, so that the size of each data set is nearly the same.


In this paper, we present an efficient joint model of structure identification and nuclearity recognition on macro level discourse structure analysis.

various kinds of feature sets are introduced to improve the performance of local models, and various constraints are introduced to improve the performance of joint model.


Then we concatenate them to get the intermediate state For Arg1 and Arg2, we encode them into the contextual representations by Bi-LSTM.

are the intermediate states of i-th word in Arg1 and j-th word in Arg2 respectively, where .


Second, the implicit syntax feature extraction method is slightly better than explicit Tree-LSTM.

the model can achieve the best performances by using the MLP outputs.


In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document.

we explore two types of caches: a dynamic cache, which stores words from the best translation hypotheses of preceding sentences, and a topic cache, which maintains a set of target-side topical words that are semantically related to the document to be translated.


Partially inspired by the success of cache models in SMT, we propose a cache-based approach to capturing coherence for neural machine translation.

we incorporate two types of caches into NMT: a static topic cache and a dynamic cache being updated on the fly.


We then use the attention network described in Equation 4  When we translate the current sentence B, we have to make sure that the decoder is provided with sufficient information from sentence B, and at the same time, with helpful imformation from the preceding sentence A.

we need a mechanism to control the scale of information flowing from the sentence A and sentence B to the decoder.


The primary reason why we use subword units is to generate rare or unknown words on the decoder side.

our purpose is to change the vocabulary at the decoder output layer into subwords.


Motivated by those outcomes, in this work we explore in more detail the practical feasibility of multilingual and zeroshot approaches.

we explore the benefit of adding training data involving related languages in a zero-shot setting and, in that framework, we compare the behavior of state-of-the-art Transformer and Recurrent NMT models.


input feeding), to predict the next word, until the <eos> symbol is generated .

the transformer architecture works by relying on a self-attention (intra-attention) mechanism, removing all the recurrent operations that are found in the previous approach.the attention mechanism is repurposed to compute the latent space representation of both the encoder and the decoder sides.


Looking at the BLEU/TER scores, it is evident that Transformer performs better in all the three model variants.

for the multilingual and the zero-shot models, the gain is statistically significant.


In unrelated language directions, our experimental setting is aimed at evaluating the impact of source language-relatedness with the target.

we focus on the zero-shot setup given its intrinsic difficulty, by taking the bilingual systems as references.


This is also confirmed by comparing performance of Recurrent models across the ZST (Table 6), ZST A and ZST B conditions.

variations from the bilingual reference model, show significant degradation when some language directions are removed (from +27.07 to +33.81) and a significant improvement when two related languages are added (from +33.81 to +26.00).


Actually, a similar trend occurs in Ro→De (Table 8), but with a relatively higher error reduction in case of the Transformer model.

the Transformer model shows reductions of −2.29 points for ZST A and −4.53 for ZST B, whereas for the Recurrent model the improvements are slightly lower, namely −1.11 (ZST A) and −4.14 (ZST B) points.


However, in the related language direction the most interesting aspect is observed with the discount of error in the Nl→It direction (Table 9).

the ZST B zero-shot model showed >2.0% error reduction over the ZST A model.


(a) one-shot: In this type of attack, the adversary manipulates all the words in the text with the best operation in parallel.

the best operation for each word is picked locally and independently of other words.


It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion.

we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise.


Given neighbor mentions N (m i ), we extract two types of vectorial global features and structured global features for each candidate e j ∈ Φ(m i ): Neighbor Mention Compatibility Suppose neighbor mentions are topical coherent, a candidate entity shall also be compatible with neighbor mentions if it has a high compatibility score with the current mention, otherwise not.

we extract the vectorial global features by computing the similarities between e j and all neighbor mentions: {sim(e j , m i )|m i ∈ N (m i )}, where m i is the mention embedding by averaging the global vectors of words in its surface form: are tokenized words of mention m j .


In this section, we investigate the effectiveness of NCEL in the "easy" and "hard" datasets, respectively.

tAC2010, which has two mentions per document on average (Section 5.1) and high prior probabilities of correct candidates (Figure 3), is regarded as the "easy" case for EL, and WW is the "hard" case since it has the most mentions with balanced prior probabilities (Guo and Barbosa, 2017).


Compared with NCEL-local, the global module in NCEL brings more improvements in the "hard" case than that for "easy" dataset, because local features are discriminative enough in most cases of TAC2010, and global information becomes quite helpful when local features cannot handle.

our propose collective model is robust and shows a good generalization ability to difficult EL.


The mapping rule r S for a structured representation S consists of matchers which decide how an unlabeled mention is mapped to S, resulting in a labeled entity mention.

a mapping rule r S is a sequence of matchers in the semantic units in S, denoted by r S = {p i | l i , p i ∈ S}.


Since surface-string similarity metrics won't suffice, we estimate structural similarity of two mentions as a function of the matchers that constitute their structures.

we compute structural similarity c(i, j) of a pair of mentions (i, j) as the edit distance of their structures, S i and S j .


Following the intuition that generic rules are less reliable, we estimate reliability of a rule based on its expected numbers of matches in the unlabeled mentions.

reliability P r S is a function of the selectivity of the matchers in r S .


LUSTRE effectively integrates this feedback to avoid over-estimating the reliability of learned rules, thereby improving the quality of model M .

it updates the reliability of a mapping rule P i r S as a function of number of incorrect predictions m r for the rule in the set of 10 predictions presented to the user (Line 20 of Algorithm 1).


We use SVM at the leaf-level to classify an entity to fine-grained categories because SVM is good for classification tasks.

we use a training dataset (containing FG-NER tagged sentences) to train a CRF model to tag the input sentences with the top-level ENE categories in Figure 2.


In the spirit of taking advantage of available dictionaries to improve our model as in CRF+SVM, we propose to integrate statistical information from the dictionary to improve the word embeddings.

for a word, we form a vector in which each element is the probability that this word is contained in an entity category, as follows: where x i is the probability that word w is contained in i th category (c i ) : We call these "entity category embeddings".


To find out the relation between the training data size with the FG-NER models, we varied the training data size and measured the F-score of each model on the test dataset.

we randomly sample the training dataset by the rates of 20%, 40%, 60%, 80% and 100% (i.e., using the entire training dataset).


It proposes an extractive MDS algorithm that maximizes the information coverage and saliency and minimizes the redundancy in the resulting summary using ACO.

it uses an ant colony system (ACS) to search for a good summary that optimizes these objectives.


The reinforcement algorithm is then applied to the graphs.

the score of each word is computed by applying a method similar to that of the HITS algorithm is applied to the first graph and a method similar to that of the PageRank algorithm is applied to the second and third graphs.


Regarding the maximization of the coverage objective (i.e., minimization of the travel distance), the heuristic value to include a new sentence (i.e., to go to a new node) is the inverse of the content score of this sentence.

the heuristic value to travel from sentence r to sentence u is computed as follows: where c u represents the content score of sentence u.


We propose in this work to exploit another social medium with language data, the Mastodon network, which closely resembles Twitter, except for two important differences: Mastodon is a decentralized social network, and it adopts permissive licenses that are compatible with reproducible research.

everyone may create his own Mastodon server with his cohort of registered users, and have his server automatically federated within the worldwide social network.


(2014) who achieved strong perplexity results compared to other standard LSTM-based neural language models.

we used a 2-layer LSTM with a 50% dropout ratio.


As can be seen, AND-LM exhibits relatively bad performance.

to make the model converge when trained on the WIKI dataset, we had to follow the heuristic suggested by Chen et al.


For each compared model, we chose the α value that showed the best self-normalization performance without sacrificing significant perplexity performance.

we used α DEV-LM = 1.0 and α NCE-R-LM = 10.0 for both PTB and WIKI datasets, and then α AND-LM = 1.0 and α AND-LM = 0.1 for the PTB and WIKI datasets, respectively.


Each review contains a list of aspect terms and corresponding polarities, which are labeled with {positive, negative, neutral}.

each aspect term has its character index in the sentence, so that when different aspect term have the same word in a sentence, we can mark the relative position distance of a sentence according to the current aspect term without confusion.


Furthermore, both AE-LSTM and ATAE-LSTM perform better than LSTM model, because they all consider the importance of the aspect term, and utilize the attention mechanism.

aTaE-LSTM outperforms aE-LSTM since it appends the aspect embedding to each word embedding and takes them as inputs, which helps the model obtain more semantic information related to aspect term.


Our proposed approach is generic and can be extended to address these variants of text adaptation while accounting for the context of generation.

we show the application to the tasks of text simplification and text readability enhancement.


In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs.

the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement.


Predicate and Arguments To recognize the predicate and arguments which identify an event, we extend a dependency-based method (Rudinger and Van Durme, 2014).


We define the degree of alignment between G p and G qa as the maximum sum of the degrees of match between aligned events in G p and G qa , which is formulated as a variant of the maximum weighted bipartite matching problem, which in turn is a special case of a 0-1 linear program.

let V p and V qa be the events in G p and G qa , respectively.


We propose to let GTCR self-adaptively enhance existing methods as an add-on.

gTCR will answer a question only if it accepts the type of the question and is confident in the scores it generates.


As GTCR relies on the results of dependency parsing, it will not be confident in the scores it generates if it finds errors in the dependency tree of the question.


RACE (Lai et al., 2017) consists of nearly 28,000 passages and 100,000 questions collected from English exams for middle and high school Chinese students.

25.8% of these questions require multi-sentence reasoning, though not necessarily temporal or causal reasoning.


NSCL is an approach that marries both the pivot-based methods and autoencoders.

we use the original implementation 3 of the Autoencoder SCL with similarity regularization, which we refer to as NSCL.


In this section we examine aspects of our model in an attempt to shed light on its strengths and weaknesses.

we observe how our model performs on highly divergent datasets, perform an error analysis, motivate our choice of projection lexicon, and motivate the need for M .


Train/dev/test splits For the PE corpus, we use the same split into training and test data as in .

our test data comprises 80 documents ("essays") with a total of 29,537 tokens (en).


It is a common approach to enable an EncDec model to work with OOVs that utilizing a special and universal symbol unk to replace all OOVs.

all OOVs share a same embedding vector in the source side and they will never be correctly generated in the target side.


The t th dimension a it denotes the attention weight of the t th word w it in sentence S i .

a it is computed as follows: The learned attention vector a i contains different weights for the corresponding words in sentence S i .


The probability is computed by a softmax function: To jointly supervise the classification and attention, we introduce a soft constraint method that is employed by other tasks with supervised attention .

it is defined as follows: where the classification loss function is cross entropy.


We introduce a new paradigm of open-domain event detection, whose goal is to detect all kinds of events.

the differences from prior work are events in unrestricted domains and with wider coverage.


The performance of semantic parsers critically depends on the quality of lexicon, including accuracy and coverage.

in order to construct the logical form from a sentence, we first need to learn a lexicon, 1 which contains the mappings from natural language phrases (e.g., "born") to logical predicates (e.g., PlaceOfBirth).


To achieve the above goal,we employ a graph-based semi-supervised learning framework, which learns lexicons not only used the alignments between Freebase and text, but also the semantic relatedness between phrases in the text side.

we use the abundant lexical resources for high coverage lexicon learning ( Figure 2 shows the difference).


This similarity plays two roles in our lexicon learning: (1) it is used for label propagation; (2) the similarity is used as a constraint on smoothing.

since we assume similar words will map to similar labels, the similarity can then strengthen the correct mapping and weaken the wrong mapping.


They utilize pattern matching to extend the lexicon.

they define knowledge patterns from knowledge bases, and text patterns from a search engine.


In this paper, we use the (phrase, predicate) mappings as seeds, then learn new (phrase, predicate) mappings by propagating mapping information through similarities between phrases.

we propose a graph-based semi-supervised approach to resolve this problem.


Next, we select high quality lexicons by scoring each lexical entry.

we use the lexicon gained in first step to train a semantic parser, and define several features to measure the quality of each lexical entry.


we use PPDB-2.0 (Pavlick et al., 2015) to calculate the similarity between two phrases by utilizing their scores, which consider many aspects.


Hence, we explore the effectiveness of compositionality of word embeddings in developing a model-free automatic metric to evaluate summary content quality.

our approach incorporates the word embedding models trained on the Google News corpus and the WordNet lexical resource to compare centroid vectors of the input and summary.


Since the constraints of WordNet lexical resource can be formalized as constraints on embeddings, we can use embeddings of non-word data types (i.e., senses).

we compute the embedding of a word by averaging the embeddings of its senses in WordNet.


SVR model aims to learn a function f : R n → R, which will be used to predict the content evaluation score for each summary y ∈ R given a feature vector X ∈ R n .

given l training instances (X 1 , y 1 ), ..., (X l , y l ), the SVR model is learnt by solving the following optimization problem (Vapnik, 1999); W is a vector of feature weights; φ is a function that maps feature vectors to a new vector space of higher dimensionality to allow non-linear functions to be learnt in the original space; C > 0 and > 0 are given.


Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly.

hUARN adopts a hierarchical architecture to encode word, sentence, and document level information.


Inspired by the above analysis, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall rating jointly for document-level multi-aspect sentiment classification.

hUARN utilizes a hierarchical structure to encode word, sentence, to document level information.


The t-test results, p-values, show that there is strong evidence (with the significance level α = 0.01) to reject the null hypothesis in user-rating consistency test and user-text consistency test on TripOUR.

we observe the existence of user-rating consistency and user-text consistency in TripOUR.


To consider user-text consistency and build an aspect-specific representation, we introduce user attention and aspect attention.

we employ word (sentence) level user aspect attention to generate sentence (document) representation.


Then, two recurrent neural network architectures are employed to learn emotion-aware word representations by leveraging noisy, but large training data.

we use Long Short-Term Memory (LSTM) networks (Hochreiter and Schmidhuber, 1997) to capture the semantic information between the words of the text document as well as the emotion information provided in the form of the target label obtained through distant supervision.


The LSTM (Long Short-Term Memory) model finds a dense low dimensional representation of words by sequentially and recurrently processing each word in a document.

the inputs of the LSTM are preprocessed text documents that consist of a sequence of words and their corresponding target variable.


Then this corpus is used as training data to learn emotion-enriched word representations using LSTM.

while document-level (entire examples) labeling is used to create the training set, the embeddings get updated at individual word level.


Assuming k emotion classes, and more than one valid emotion label for each document, the target variable y is binary represented.

y j = 1 indicates presence of an emotion class, and y j = 0 otherwise.


The emotion vector a (d) for document d is then, the sum of the emotion vectors of all its words, a (d) = i∈d a (w i ).

if the document has an association with at least one emotion, i.e., ∃j, a j (d) > 0, then, S = argmax i a (d), where S ⊆ L. documents assigned zero emotion score are not considered.


According to the evaluation results, AE + GRL + C has the overall preference of the raters in all three dimensions.

given that we are interested in semantic preservation, this model is evaluated better than a style-transfer model that has some access to semantic relations between source and target on the SEMANTICS dimension.


Most tree transformations used in parsing have been hard-coded into the training and parsing algorithms.

that was the case in the Collins implementation and even in Bikel's version.


We used this application in this paper to show that tsed language is sufficiently powerful so that it can be used to replace transformations originally made by programming language code pieces, that were never subject to a restriction in power of expression.

collins defined and coded the transformations that he thought as useful and that he could mechanically express having all the Turing machine power of a general programming language at his disposition.


But in the development and test datasets, each instance is a four-sentence story and two candidate endings (one is the right ending and the other is the wrong ending).

the wrong endings are purposely designed to fit to the story context but logically wrong.


Turney (2003) used statistical association between seed words and unseen words to induce SO.

the point-wise mutual information (PMI) of word co-occurrence statistics was used to find similarities of a word to a seed set.


(2015) who also provide a useful overview of hyper parameters in each of the discussed methods.

this led us to run 500-dimensional SVD-embeddings with a context-window of   (Velikovich et al., 2010) with context-distribution smoothing and eigenvalue weighting (Levy et al., 2015).


To enable the model to perform these three types of edit operations, we use two distinct decoders, one for delete and the other for copy and generate.

we use the same encoder as in the general Seq2Seq model, but employ a delete decoder and a copy-generate decoder.


The Copy-Generate decoder produces output the compressed sentence word by word, and the output words are either copied from the input words which are filtered by the delete decoder, or generated with a fixed vocabulary.

our model integrates copy, generate, delete operations together to produce the output sequence.


Otherwise, if generate mode is chosen, the word is generated from the fixed vocabulary.

for words that are neither in the source sentence nor in the vocabulary, the switch network will choose generate mode and UNK tokens will be produced from the decoder.


The alignment information together with the pair of source text and compressed text are used for generating operation sequences for our experiments.

for each pair of the source and compressed text, a delete operation sequence and a copy/generate sequence are generated.


In order to evaluate the quality of the summaries produced by our model and the other baselines, we ask annotators to do a score evaluation on some aspects of the summaries.

we ask them to score the grammaticality and non-redundancy of the summaries.


In this paper, we propose a novel deep memory network with an auxiliary memory (DAuM) to solve the above problems.

we first adopts a basic memory to capture the important part of context words for sentiment classification.


Input Embedding: To apply deep-learning method to text data, we first map each word into a lowdimensional continuous vector, which is also known as word embedding (Mikolov et al., 2013;Pennington et al., 2014).

let L ∈ R d×|V | be an embedding matrix made up of all the word embeddings, where d is the dimensionality of word embedding and |V | is vocabulary size.


Inspired by (Lau et al., 2017), we believe that the aspect representation generated via a term should be able to predict the term in turn.

the term embedding is more similar to the aspect representation than the embeddings of other words in the sentence.


(2013) to a graph in order to create a representation for each of its nodes based on their positions and their neighbors.

given a graph with nodes V = {v 1 , v 2 , .


This work takes the lead to study the aspect/sentiment-aware abstractive review summarization in an end-to-end manner without hand-crafted features and templates by exploring the encoder-decoder framework and multi-factor attentions.

we propose a mutual attention mechanism to interactively learns the representations of context words, sentiment words and aspect words within the reviews, acted as an encoder.


Our model exploits the recent success of the encoder-decoder framework to generate aspect/sentiment-aware review summaries.

a mutual attention mechanism is proposed to capture the correlation of context words, sentiment words and aspect words, which interactively learns attentions in the three kinds of words and generates the representations for contexts, sentiments and aspects separately.


LSTM+SynATT performs the best on all groups.

it performs substantially better on groups with two or three targets.


We evaluate our model on the SemEval 2010 Task 8 dataset which includes 19 relation classes.

we use F1 score to measure the quality of extracted rationales and the classification performance.


Selected rationales with target entities  6 Discussion and Future Goals The major difference between this work and the previous rationale-based research is that we designed the Generator without a sequential RNN model.

we developed a layer by layer process that considers different levels of text information for three reasons.


In this paper, we implement the sentence encoder with both convolutional (CNN) and recurrent (RNN) architectures.

we set the encoders E I j and E C j to encode each sentence in the j-th language into its individual and consistent embeddings respectively, and expect these embeddings to capture the diversity and consistency among languages.


2016proposed a bidirectional neural network BRCNN, which consists of two RCNNs that can learn features along SDP inversely at the same time.

information of words and dependency relations is extracted by a two-channel RNN model with LSTM units.


It builds a relation classification model based on SDP, and can take full advantages of both the CNN model and the RNN model.

given a sentence and its dependency tree, the BRCNN model first extracts a SDP from the dependency tree.


Thus as long as a word is in SDP, it must be much important.

the role of SDP and the attention method overlaps to a certain extent.


Thus, the role of WordNet decreases when text descriptions are used.

in a relation classification task, text descriptions can replace WordNet to a certain extent.


We describe our major system components in this section.

content planning ( §3.1) takes as input a set of similar sentences.


(1) presents a scoring function for any graph G. It can be factorized into a sum of scores for selected nodes and edges.

edge score 1Features characterizing the graph nodes and edges are adopted from .


We gauge how best to select source sentences by exploring different similarity metrics.

(i) LCS calculates the longest common subsequence between a candidate source sentence and the reference sentence; (ii) VSM represents sentences using the vector space model and calculates the cosine similarity between the two sentence vectors; (iii) Smatch (Cai and Knight, 2013) calculates the F-score of AMR concepts between the candidate and reference sentences; (iv) Concept Coverage selects source sentences to maximize the coverage of AMR concepts of the reference sentence.


We observe that the quality of AMR parsers has an impact on summarization performance.

jAMR reports 58% F-score and CAMR reports 63% F-score for parsing sentences.


On the one hand, the VRALSTM models (trained from Scratch or trained adapting model from Source domains) produce the outputs with a diverse range of error types, including missing, misplaced, redundant, wrong slots, or even spelling mistake information, leading to a very high of the slot error rate ERR score.

the VRALSTM from Scratch tends to make repeated slots and also many of the missing slots in the generated outputs since the training data may inadequate for the model to generally Table 4: Comparison of top Laptop responses generated for different scenarios by adaptation training VRALSTM (denoted by ) and VDANLG (denoted by ) models from Source domains, and by training VRALSTM from Scratch.